@misc{11495, abstract = {{To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose. }}, author = {{Kruse, Julia and Wörner, Julius and Schneider, Jan and Dörksen, Helene and Pein-Hackelbusch, Miriam}}, booktitle = {{Sensors}}, issn = {{1424-8220 }}, keywords = {{multidimensional sensor arrays, MOS sensors, beer fermentation, process control, gas analysis, metal oxide semiconductors, intentional data analysis, chemometrics, PLSR, PCA, first-order calibration}}, number = {{11}}, publisher = {{MDPI}}, title = {{{Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses }}}, doi = {{10.3390/s24113520}}, volume = {{24}}, year = {{2024}}, } @misc{11982, abstract = {{The aim of this study was to investigate if vibroacoustic methods may be used for the non-destructive determination of beef during its aging process. The vibroacoustic method was based on the observation of mechanical changes in the meat during the aging process and was compared with reference data obtained by Warner-Bratzler shear force measurement as well as sensory testing of the tenderness using a ten-part scale. To evaluate the mechanical properties, transfer functions were used representing the time dependency of the signal and thus the viscoelastic behaviour. In this study, a total of 31 roastbeef samples from 16 different young bulls and two older cows were examined from day of slaughter to day 21 of cold storage with regard to their tenderness. For this purpose, vibroacoustic measurements were carried out on the unprocessed/raw meat at intervals of 1–3 days. The reference measurements using sensor technology and Warner-Bratzler shear force measurement were carried out on the first (day of slaughter) and last (21st day) day on slices of roast beef cooked with saturated steam. In the results of all three methods, the shear force measurement, the sensory test and the vibroacoustic method, showed that roastbeef from the same animal but different halves produced different results. Basically, it is possible to predict the tenderness of roastbeef by taking measurements at the beginning of the maturing process for the end of the maturing period using vibroacoustic methods: Data analysis led to a trend function that roughly reflects the actual tenderness, which is generally higher than the real tenderness represented by the shear-force measurement. In order to obtain a better resolution for recording the mechanical changes during the aging process, the measurements should be carried out at shorter intervals.}}, author = {{Tholen, Janna and Gohe, Jan and Dörksen, Helene and Kiesel, Theo and Upmann, Matthias}}, booktitle = {{Food Physics}}, issn = {{2950-0699}}, keywords = {{Warner-Bratzler shear force, Vibroacoustic methods, Non-destructively measurement, Viscoelastic meat}}, number = {{1}}, publisher = {{Elsevier}}, title = {{{Tenderness prediction for beef using novel datea analysis methods based on system dynamic and acoustic signals}}}, doi = {{10.1016/j.foodp.2024.100017}}, year = {{2024}}, } @misc{13327, abstract = {{The aim of this study was to investigate if vibroacoustic methods may be used for the non-destructive determination of beef during its aging process. The vibroacoustic method was based on the observation of mechanical changes in the meat during the aging process and was compared with reference data obtained by Warner-Bratzler shear force measurement as well as sensory testing of the tenderness using a ten-part scale. To evaluate the mechanical properties, transfer functions were used representing the time dependency of the signal and thus the viscoelastic behaviour. In this study, a total of 31 roastbeef samples from 16 different young bulls and two older cows were examined from day of slaughter to day 21 of cold storage with regard to their tenderness. For this purpose, vibroacoustic measurements were carried out on the unprocessed/raw meat at intervals of 1–3 days. The reference measurements using sensor technology and Warner-Bratzler shear force measurement were carried out on the first (day of slaughter) and last (21st day) day on slices of roast beef cooked with saturated steam. In the results of all three methods, the shear force measurement, the sensory test and the vibroacoustic method, showed that roastbeef from the same animal but different halves produced different results. Basically, it is possible to predict the tenderness of roastbeef by taking measurements at the beginning of the maturing process for the end of the maturing period using vibroacoustic methods: Data analysis led to a trend function that roughly reflects the actual tenderness, which is generally higher than the real tenderness represented by the shear-force measurement. In order to obtain a better resolution for recording the mechanical changes during the aging process, the measurements should be carried out at shorter intervals.}}, author = {{Tholen, Janna and Gohe, Jan and Dörksen, Helene and Kiesel, Theo and Upmann, Matthias}}, booktitle = {{Food Physics}}, issn = {{2950-0699}}, keywords = {{Warner-Bratzler shear force, Vibroacoustic methods, Non-destructively measurement, Viscoelastic meat}}, number = {{9}}, publisher = {{Elsevier BV}}, title = {{{Tenderness prediction for beef using novel data analysis methods based on system dynamic and acoustic signals}}}, doi = {{10.1016/j.foodp.2024.100017}}, volume = {{1}}, year = {{2024}}, } @misc{10326, abstract = {{In the food industry, and especially in wines as products thereof, ethanol and sulfur dioxide play an equally important role. Both substances are important wine quality characteristics as they influence the taste and odor. As both substances comprise volatile matter, electronic noses should be applicable to discriminate the different qualities of wines. Our study investigates the influence of alcohol and sulfur dioxide on the discrimination ability of wines (especially those of the same grape variety) using two different electronic nose systems. One system is equipped with metal oxide sensors and the other with quartz crystal microbalance sensors. Contrary to indications in literature, where the alcohol content is discussed to have a large influence on e-nose results, it was shown that a difference of 1 % ethanol was not sufficient to allow accurate discrimination using Linear Discriminant Analysis by any system. On the positive side, the analyzed concentrations of ethanol (about 12 %) did not superimpose other volatile information. So difference in sulfur dioxide content gave an accuracy for sample discrimination of up to 90.6 % with MOS nose. Thus, we are so far partially able to discriminate wines with electronic noses based on their volatile imprint.}}, author = {{Wörner, Julius and Dörksen, Helene and Pein-Hackelbusch, Miriam}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, keywords = {{Ethanol, Pipelines, Metals, Nose, Electronic noses, Sensor systems, Sensors, Quartz crystals, Linear discriminant analysis, Sulfur}}, location = {{Lemgo}}, title = {{{Key Indicators for the Discrimination of Wines by Electronic Noses}}}, doi = {{https://doi.org/10.1109/INDIN51400.2023.10217912}}, year = {{2023}}, } @misc{12706, abstract = {{Vaseline, also referred to as petrolatum, is a colloidal dispersion of liquid-crystalline structures of hydrocarbons derived from petroleum. It has long been recognized for its versatile applications in the pharmaceutical industry, with its use in the formulation of various topical medications, wound care products, and drug delivery systems. For pharmaceutical use, petrolatum has to meet the quality standards described in its Pharmacopoeia monograph. The comprised test ranges allow for a broad range of Vaseline qualities on the market, while the tests themselves only poorly discriminate between grades. The only differentiating properties are related to the melting behavior, which is tested via drop point analysis, and the consistency, addressed in the functionality-related characteristics section. In this study, we propose the hypothesis that Near-infrared spectroscopy (NIRS) could be a comparably simple method to evaluate the crystalline behavior of Vaseline qualities. We expect such information to provide additional details for Vaseline quality discrimination. This discrimination would allow the most suitable petroleum jelly to be selected for an existing formulation when the previous one needs to be replaced; for example, due to a manufacturer change. We demonstrate that NIRS in transmission and reflectance mode obtained by traditional continuous spectra acquisition and fragmented NIR spectra acquisition through multi-optical, multi-modal excitation, respectively, can both serve as a basis for detecting Vaseline quality differences, which we have further proven by thermal analysis and tests with semisolid formulations. Additionally, we demonstrate that a lower-cost multi-optical spectrometer in reflectance mode can detect Vaseline quality differences in rotated samples.}}, author = {{Fliedner, Niels Hendrik and Lohweg, Volker and Al-Karawi, Claudia and Pein-Hackelbusch, Miriam}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Dörksen, Helene and Scanzio, Stefano and Jasperneite, Jürgen and Wisniewski, Lukasz and Man, Kim Fung and Sauter, Thilo and Seno, Lucia and Trsek, Henning and Vyatkin, Valeriy }}, isbn = {{978-1-6654-9314-7}}, keywords = {{multimodal sensing, crystalline materials, microstructure, rotation measurement, PCA, calorimetry, pharmaceuticals, European Pharmacopoeia}}, location = {{Lemgo}}, publisher = {{IEEE}}, title = {{{A Novel Spectroscopic Approach for Vaseline Quality Discrimination}}}, doi = {{10.1109/indin51400.2023.10218318}}, year = {{2023}}, } @misc{12785, abstract = {{Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation.}}, author = {{Wunderlich, Paul and Wiegräbe, Frauke and Dörksen, Helene}}, booktitle = {{INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH}}, issn = {{1660-4601}}, keywords = {{machine learning, healthcare, case management, caring, multi-label classification}}, number = {{2}}, publisher = {{MDPI}}, title = {{{Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance}}}, doi = {{10.3390/ijerph20021215}}, volume = {{20}}, year = {{2023}}, } @misc{12873, abstract = {{Reliable Banknote Authentication is critical for economic stability. Regarding everyday use, recent studies implemented successful techniques using banknote images taken by mobile phone cameras. One challenge in mobile banknote authentication is that it is impossible to collect images by all series/brands of mobile phones. In this study, classification models are implemented that are able to generalize to the samples from a wide number of mobile phone series even though they are trained with samples from a small group of series. Existing state-of-the-art banknote authentication approaches train a separate model per sub-image of a banknote, using the extracted features of that sub-image. A new approach that trains a single global model on the concatenated features of all the sub-images is presented. Furthermore, ensemble models that combine Linear Discriminant Analysis and Deep Neural Networks are employed in order to maximize the accuracy. Implemented techniques were able to reach up to F1-score of 0.99914 on a Euro banknote data set which contain images from 16 different mobile-phone series. The results also indicate that new global model approach can improve the accuracy of the existing banknote authentication techniques in case of model training with images from restricted/incomplete phone series and brands.}}, author = {{Sürmeli, Baris Gün and Gillich, Eugen and Dörksen, Helene}}, booktitle = {{Artificial Neural Networks and Machine Learning - ICANN 2023}}, editor = {{Iliadis, Lazaros }}, isbn = {{978-3-031-44209-4}}, issn = {{1611-3349}}, location = {{Heraklion, GREECE}}, pages = {{332--343}}, publisher = {{Springer Nature Switzerland}}, title = {{{Generalisation Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples}}}, doi = {{10.1007/978-3-031-44210-0_27}}, volume = {{14255}}, year = {{2023}}, } @misc{13015, abstract = {{food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.}}, author = {{Wunderlich, Paul and Pauli, Daniel and Neumaier, Michael and Wisser, Stephanie and Danneel, Hans-Jürgen and Lohweg, Volker and Dörksen, Helene}}, booktitle = {{Foods}}, issn = {{2304-8158}}, keywords = {{Plant Science, Health Professions (miscellaneous), Health (social science), Microbiology, Food Science}}, number = {{6}}, publisher = {{MDPI }}, title = {{{Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors}}}, doi = {{10.3390/foods12061347}}, volume = {{12}}, year = {{2023}}, } @inproceedings{1991, author = {{Funk, Mark and Scharf, Matthias and Dörksen, Helene and Danneel, Hans-Jürgen and Lohweg, Volker and Hübner, Michael and Schaede, Johannes and Stierman, Rob and Knobloch, Alexander and Le, Dinh Khoi and Gillich, Eugen and Mönks, Uwe}}, booktitle = {{ODS 2020 Review}}, location = {{San Francisco}}, title = {{{Creating a Self-authentication System for Smart Banknotes}}}, year = {{2020}}, } @inproceedings{1992, author = {{Meier, Philip and Lohweg, Volker and Dörksen, Helene and Schaede, Johannes}}, booktitle = {{Optical Document Security (ODS)}}, title = {{{Intaglio Style Transfer – Partially Automating the Intaglio Image Creation}}}, year = {{2020}}, } @inproceedings{1996, abstract = {{Intelligent technical systems need to become more flexible and adaptive in the context of Cyber-Physical-Systems and Industry 4.0. Cash supply is very important for the worldwide economy. Within the supply chain, handling processes are highly automated, e.g., automated teller machines are installed to ensure an efficient and reliable cash supply. Recently, there is a trend in automated cash handling systems to replace the cash cassettes for each denomination by a simple pitch-in-plastic-bag for all banknotes. This necessitates new automated handling approaches because the bag content will unload irregularly. We propose a new approach which is able to detect the pose and possible occlusions of randomly distributed textured banknotes with simple smartphone cameras. This information can be used to control a handling robot-picking out banknotes.}}, author = {{Gillich, Eugen and Fritze, Alexander and Henning, Kai-Fabian and Pfeifer, Anton and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{5th IEEE International Forum on Research and Technologies for Society and Industry}}, title = {{{Camera-based Occlusion Detection for Banknote Sorting}}}, year = {{2019}}, } @inproceedings{2000, author = {{Pfeifer, Anton and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Digital Document Security}}, title = {{{Effective Protection of Physical Documents by Print Coding as Digital Tag and Authentication Methods}}}, year = {{2019}}, } @inproceedings{2005, abstract = {{We present a method for the fast and robust linear classification of badly conditioned data. In our considerations, badly conditioned data are such data which are numerically difficult to handle. Due to, e.g. a large number of features or a large number of objects representing classes as well as noise, outliers or incompleteness, the common software computation of the discriminating linear combination of features between classes fails or is extremely time consuming. The theoretical foundations of our approach are based on the single feature ranking, which allows fast calculation of the approximative initial classification boundary. For the increasing of classification accuracy of this boundary, the refinement is performed in the lower dimensional space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental results indicate high classification accuracy of the approach. For the modern real industrial applications such a method is especially suitable in the Cyber-Physical-System environments and provides a part of the workflow for the automated classifier design}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, keywords = {{Task analysis, Software, Linear discriminant analysis, Dimensionality reduction, Mathematical model, Covariance matrices, Measurement}}, location = {{ Turin, Italy }}, title = {{{Linear Classification of Badly Conditioned Data. }}}, doi = {{10.1109/ETFA.2018.8502485}}, year = {{2018}}, } @inproceedings{2008, abstract = {{We concentrate our research activities on the multivariate feature selection, which is one important part of many machine learning tasks. In partucular, Linear Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate analysis. From the theoretical point of view, it is the well-known fact that LDA is best suitable in the case the features are Gaussian distributed. In the theoretical part of the presented paper, we analyse the properties of the multivariate discriminant analysis with respect to the feature selection. In this context, we consider a binary supervised learning task and assume that the features are Gaussian distributed. The discriminant analysis solves the mentioned supervised learning task by maximising of the discriminant value, calculated for the linear combination of the features. The initial LDA solution a 2 Rd is considered for all given features from the feature space X  Rd. The corresponding discriminant is calculated by the formula: d(a; x1, . . . , xd) := (μ+ − μ−)2 2+ + 2− , where μ+/− are projected class means and 2 +/− are projected class variances (with respect to a). We proof several propositions with the aim to find subsets of the features having higher discriminant value as original d(a; x1, . . . , xd). For the suitability in the real world settings, here we are interested in fast searching for such subsets. The performance of the mentioned propositions is examined experimentally on datasets from UCI repository [2]. Several application scenarien will be discussed and tested on the datasets. In addition, tests show that the performance can be achieved also in the case the features are not Gaussian distributed.}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{European Conference on Data Analysis (ECDA2018)}}, keywords = {{multivariate feature selection, Gaussian distribution, linear discriminant analysis}}, location = {{Paderborn}}, title = {{{Multivariate Gaussian Feature Selection. }}}, year = {{2018}}, } @inproceedings{2011, author = {{Lohweg, Volker and Funk, Mark and Scharf, Matthias and Dörksen, Helene and Danneel, Hans-Jürgen and Hübner, Michael and Schaede, Johannes and Thony, Emmanuel and Knobloch, Alexander and Lee, Dinh Khoi and Mönks, Uwe and Gillich, Eugen}}, booktitle = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection XII San Francisco}}, location = {{San Francisco, USA}}, title = {{{smartBN—Intelligent Protection and Authentication in Payment Transactions by Smart Banknotes}}}, year = {{2018}}, } @inproceedings{2015, abstract = {{ In real-world scenarios it is not always possible to generate an appropriate number of measured objects for machine learning tasks. At the learning stage, for small/incomplete datasets it is nonetheless often possible to get high accuracies for several arbitrarily chosen classifiers. The fact is that many classifiers might perform accurately, but decision boundaries might be inadequate. In this situation, the decision supported by marginlike characteristics for the discrimination of classes might be taken into account. Accuracy as an exclusive measure is often not sufficient. To contribute to the solution of this problem, we present a margin-based approach originated from an existing refinement procedure. In our method, margin value is considered as optimisation criterion for the refinement of SVM models. The performance of the approach is evaluated on a real-world application dataset for Motor Drive Diagnosis coming from the field of intelligent autonomous systems in the context of I ndustry 4.0 paradigm as well as on several UCI Repository samples with different numbers of features and objects.}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods}}, isbn = {{9789897582226}}, keywords = {{Refinement of Classification, Robust Classification, Classification within Small/Incomplete Samples}}, location = {{Porto, Protugal}}, pages = {{293--300}}, publisher = {{SCITEPRESS - Science and Technology Publications, Lda.}}, title = {{{Margin-based Refinement for Support-Vector-Machine Classification}}}, doi = {{10.5220/0006115502930300}}, year = {{2017}}, } @inproceedings{2019, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{4th European Conference on Data Analysis 2017 (ECDA 2017) }}, location = {{Wroclaw, Poland}}, title = {{{Margin-based Refinement for Linear Discriminant Analysis}}}, year = {{2017}}, } @inproceedings{2040, abstract = {{Banknote authentication plays a fundamental role in banknote circulation. Banknotes undergo some major changes in thenext years because electronic payment systems will get more common which will change the user behaviour. Furthermore,technologies of counterfeiters will improve progressively and continuously. We propose a two-sage procedure for theeffective development for the design of simple electronics for cash handliung systems: First, we establish a high qualityimage acquisition system which allows for a consistent image capture quality and is able to handle complex softwarealgorithms for banknote authentication. Second, we take the algorithms and port them on cost-efficient hardware. It isshown in this paper that a reliable authentication of almost each banknote is possible for to specialised low-cost systemssuch as point-of-sale cash units.}}, author = {{Gillich, Eugen and Hoffmann, Jan Leif and Dörksen, Helene and Lohweg, Volker and Schaede, Johannes}}, booktitle = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection V}}, title = {{{Data Collection Unit – A Platform for Printing Process Authentication}}}, year = {{2016}}, } @article{2044, abstract = {{Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.}}, author = {{Mönks, Uwe and Dörksen, Helene and Lohweg, Volker and Hübner, Michael}}, issn = {{1424-8220}}, journal = {{Sensors}}, title = {{{Information Fusion of Conflicting Input Data}}}, doi = {{10.3390/s16111798}}, year = {{2016}}, } @inproceedings{2125, author = {{Deppe, Sahar and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Workshop on Probabilistic Graphical Models}}, title = {{{Multi-Scale Motif Discovery in Image Processing}}}, year = {{2015}}, } @inproceedings{2128, abstract = {{We present the concept of a perceptive motor in terms of a cyber-physical system (CPS). A model application monitoring a knitting process was developed, where the take-off of the produced fabric is controlled by an electric motor. The idea is to equip a synchronous motor with a smart camera and appropriate image processing hard- and software components. Subsequently, the characteristics of knitted fabric are analysed by machine-learning (ML) methods. Our concept includes motor-current analysis and image processing. The aim is to implement an assistance system for the industrial large circular knitting process. An assistance system will help to shorten the retrofitting process. The concept is based on a low cost hardware approach for a smart camera, and stems from the recent development of image processing applications for mobile devices [1–4].}}, author = {{Vukovic, Kristijan and Simonis, Kristina and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Conference on Machine Learning for Cyber-Physical Systems (ML4CPS)}}, keywords = {{Assistance System, Euler Number, Synchronous Motor, Image Processing System, Image Processing Method}}, title = {{{Efficient Image Processing System for an Industrial Machine Learning Task}}}, doi = {{10.1007/978-3-662-48838-6_8}}, year = {{2015}}, } @inproceedings{2136, abstract = {{In modern industrial applications driven by Cyber-physical systems (CPS) it is a challenging task to model and optimize processes such as machine analysis and diagnosis. Since the CPS have to act autonomously, a procedure for automated decision making has to be designed. In our work we concentrate on the design of a decision procedure by a fuzzy classifier approach. For our application on decision making in an industrial environment, a fuzzy approach was picked as convenient classification technique regarding balance between accuracy and computational time. We present a supervised learning method called FUZZY-ComRef which combines fuzzy classification and our combinatorial refinement method, called ComRef [1]. Due to the fact that fuzzy classification might behave inaccurately for some datasets, the aim of our approach is to improve the results provided by the (stand-alone) fuzzy classification. We show the performance of FUZZY-ComRef evaluated on the samples from the UCI Repository and on our real-world dataset Motor Drive Diagnosis. In addition, we discuss the quadratic computational time problem arising from the combinatorial nature of ComRef. Furthermore, we show based on real-time evaluations that within parallelisation the proposed FUZZY-ComRef is suitable to many applications in CPS.}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. }}, keywords = {{Support vector machines, Accuracy, Time complexity, Decision making, Motor drives, Shape, Sensors}}, publisher = {{IEEE}}, title = {{{Automated Fuzzy Classification with Combinatorial Refinement}}}, doi = {{ 10.1109/ETFA.2015.7301514}}, year = {{2015}}, } @inproceedings{2166, author = {{Gillich, Eugen and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{IST/SPIE Electronic Imaging 2015, Image Processing: Machine Vision Applications VIII}}, pages = {{1--12}}, publisher = {{SPIE}}, title = {{{Advanced Color Processing for Mobile Devices }}}, year = {{2015}}, } @inproceedings{2147, author = {{Gillich, Eugen and Dörksen, Helene and Lohweg, Volker}}, title = {{{Generation of robust optical paths – Color Processing for Mobile Devices. }}}, year = {{2014}}, } @inproceedings{2148, author = {{Hofmann, Jürg and Gillich, Eugen and Dörksen, Helene and Chassot, Daniel and Schaede, Johannes and Türke, Thomas and Lohweg, Volker}}, title = {{{New Strategies in Image Processing for Standardized Intaglio Quality Analysis in the Printing Process.}}}, year = {{2014}}, } @inproceedings{2160, abstract = {{We present a new approach for linear classification optimisation based on Combinatorial Refinement (ComRef) of feature weighting for cognitive signal processing in resource-limited hardware and software like in Cyber-physical systems. Despite simple construction, the approach is able to connect advantages of dimensionality reduction methods and such like combining multiple classifiers resp. Bag-of-classifiers-approaches and leads to a good generalisation ability even by use of small feature sets. Regarding generalisation ability, we benchmark the performance of ComRef on several datasets from the UCI repository. Furthermore, for an industrial dataset Motor Drive Diagnosis we show the advantage of ComRef which uses Support-Vector-Machines (SVM). In this application scenario, a trustful classifier is essential, since a small number of mis-classifications could lead to motor damages.}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)}}, isbn = {{978-1-4799-4845-1}}, publisher = {{IEEE}}, title = {{{Combinatorial refinement of feature weighting for linear classification}}}, doi = {{10.1109/ETFA.2014.7005106}}, year = {{2014}}, } @inproceedings{2161, author = {{Dörksen, Helene and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona}}, title = {{{Fast Classification in Industrial Big Data Environments}}}, year = {{2014}}, } @inproceedings{2129, abstract = {{Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-of-Intaglio principle for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resource-limited, but effective CPUs, cameras with illumination, and flexible operating systems. Hence, it is obvious to apply smartphones for banknote authentication, especially for visually impaired persons. Our approach shows that those devices are capable of processing data under the constraints of image quality and processing power. Strictly a mobile device as such is not an industrial product for harsh environments, but it is possible to use mobile devices for banknote authentication. The concept is based on a new strategy for constructing adaptive Wavelets for the analysis of different print patterns on a banknote. Furthermore, a banknote specific feature vector is generated which describes an authentic banknote effectively under various illumination conditions. A multi-stage Lineardiscriminant- analysis classifier generates stable and reliable output.}}, author = {{Lohweg, Volker and Dörksen, Helene and Hoffmann, Jan Leif and Hildebrand, Roland and Gillich, Eugen and Schaede, Johannes and Hofmann, Jürg}}, booktitle = {{Media Watermarking, Security, and Forensics 2013}}, publisher = {{(03-07.02.2013) IST/SPIE Electronic Imaging 2013}}, title = {{{Banknote authentication with mobile devices}}}, doi = {{https://doi.org/10.1117/12.2001444}}, year = {{2013}}, } @article{2104, abstract = {{Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-of-Intaglio concept [1] for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resource-limited but effective CPUs, cameras with illumination, and flexible operating systems. Hence, it appears to be obvious, to apply those smartphones for banknote authentication, especially for visually impaired persons. However, it has to be researched, whether those devices are capable of processing the data under the constraints of image quality and processing power. Our results show that it is in general possible to use such devices for banknote authentication applications.}}, author = {{Lohweg, Volker and Dörksen, Helene and Gillich, Eugen and Hildebrand, Roland and Hoffmann, Jan Leif and Schaede, Johannes}}, journal = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection III}}, keywords = {{authentication, anti-counterfeit features, mobile device, smartphone, wavelet transform, pattern recognition, Sound-of-Intaglio}}, title = {{{Mobile Devices for Banknote Authentication – is it possible? In: Optical Document Security - The Conference on Optical Security and Counterfeit Detection III, San Francisco, CA, USA, January 18-20, 2012. }}}, year = {{2012}}, } @inproceedings{2116, abstract = {{Favored by hardware development, since the mid-2000s, cameras can be found in mobile phones. With the advent of the Apple iPhonethey are equipped with a multi-touch high-resolution display. Their included battery and low costs make them attractive for smart cameraapplications. This paper shows several scenarios, in which advantagesand disadvantages of smartphones are inspected. A real-life applicationis given, which shows that a phone of this kind can be used for printinspection and banknote authentication}}, author = {{Gillich, Eugen and Hildebrand, Roland and Hoffmann, Jan Leif and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{BVAu 2012 - 3. Jahreskolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}}, keywords = {{smart camera, smartphones, banknotes, authentication}}, publisher = {{inIT-Institut für industrielle Informationstechnik}}, title = {{{Smartphones as Smart Cameras – Is It Possible?}}}, year = {{2012}}, }